What are the responsibilities and job description for the Generative AI Engineer position at Realtime Recruitment?
Senior AI Engineer (Generative AI)
Full Time Permanent - Not open to contract
Remote - Preferably East Coast
Open to US Citizens - GC Holders
Join a fast-growing technology company with a data-first approach, building enterprise AI solutions that help organisations leverage cloud technologies and modern engineering to solve complex business problems.
This is a hands-on engineering role where you'll design, build, deploy, and support enterprise Generative AI applications. The focus is on taking AI solutions beyond proof of concepts and prototypes into secure, scalable, production environments. Commercial Experience only
The ideal candidate has strong software engineering fundamentals combined with extensive experience building and deploying AI applications used by real customers.
The Opportunity
As a Senior AI Engineer, you'll lead the development of enterprise AI solutions, owning the complete lifecycle from architecture and development through production deployment and ongoing optimisation.
This role is focused on application engineering, not research. You'll be expected to build production-ready software that integrates Large Language Models into enterprise applications while solving real-world challenges such as scalability, latency, security, reliability, monitoring, and cost optimisation.
Working closely with cross-functional teams, you'll translate business requirements into robust AI-powered applications that can operate reliably in production.
Key Responsibilities
- Design and develop end-to-end Generative AI applications using Python and modern AI frameworks.
- Build production-grade Retrieval-Augmented Generation (RAG) solutions and semantic search capabilities.
- Develop intelligent document processing and AI-powered automation solutions.
- Design and implement multi-agent and agentic AI workflows.
- Fine-tune and optimise LLMs and embedding models for domain-specific use cases.
- Build scalable APIs and backend services that integrate AI into enterprise applications.
- Deploy AI applications into production cloud environments using MLOps best practices.
- Implement monitoring, observability, testing, logging, versioning, and continuous improvement processes.
- Collaborate with engineering, product, and business stakeholders to deliver scalable AI solutions.
- Ensure solutions are secure, maintainable, performant, and production-ready.
Required Technical Experience
- Strong Python software development experience.
- Hands-on experience building enterprise Generative AI applications.
- Experience with LLMs such as GPT, Gemini, Claude, Llama, or similar.
- Strong knowledge of Prompt Engineering, Retrieval-Augmented Generation (RAG), and embedding models.
- Experience with AI orchestration frameworks such as LangChain, LlamaIndex, or equivalent.
- Experience with vector databases and semantic search technologies.
- Strong SQL and data engineering knowledge.
- Experience developing scalable REST APIs and backend application services.
- Experience working with cloud platforms such as Google Cloud, AWS, or Azure.
- Understanding of CI/CD, version control, testing, containerisation, and software engineering best practices.
Essential Experience
- 5 years of AI/ML or software engineering experience.
- Proven experience developing, deploying, and supporting AI applications in live production environments.
- Experience taking applications from design and development through deployment, monitoring, maintenance, and ongoing enhancement.
- Commercial experience integrating AI into enterprise software products—not just experimentation, research, proof-of-concept projects, or internal demos.
- Experience building scalable, customer-facing applications used in production.
Nice to Have
- Experience with traditional machine learning techniques and model training.
- MLOps experience.
- Docker and Kubernetes experience.
- Experience with cloud-native architectures.
What They're Looking For
The successful candidate is a software engineer with deep AI expertise who understands how to build robust, scalable applications. You'll write clean, maintainable code and be comfortable owning solutions throughout their entire lifecycle.
Most importantly, you'll have real commercial experience deploying AI applications into production. This role is not suited to candidates whose experience is limited to research, prototypes, academic projects, hackathons, or test environments.